Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [51]:
!ls -al /input
total 8308
drwxr-xr-x   4 root root    6144 Apr 29 00:27 .
drwxr-xr-x 139 root root    4096 Aug 16 16:16 ..
drwxr-xr-x   2 root root 6137856 Apr 28 19:01 img_align_celeba
drwxr-xr-x   2 root root 2365440 Apr 28 18:57 mnist
In [52]:
data_dir = '/input'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [53]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[53]:
<matplotlib.image.AxesImage at 0x7f8f8589c1d0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [54]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[54]:
<matplotlib.image.AxesImage at 0x7f8fa994d6a0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [55]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [56]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    
    real_inputs = tf.placeholder(
        tf.float32, 
        (None, image_height, image_width, image_channels),
        name='real_inputs'
    )
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), name='z_inputs')
    lrate = tf.placeholder(tf.float32, name='lrate')
    return real_inputs, z_inputs, lrate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [57]:
def discriminator(images, reuse=False, alpha=0.1, kernel=5, filters=32):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # Input layer is 28x28x3
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, filters, kernel, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x32
        
        x2 = tf.layers.conv2d(x1, filters*2, kernel, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x64
        
        x3 = tf.layers.conv2d(x2, filters*2, kernel, strides=1, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        flat = tf.reshape(relu3, (-1, 7*7*filters*2))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [58]:
def generator(z, out_channel_dim, is_train=True, alpha=0.1, kernel=5):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 7*7*512)
        
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x512
        
        x2 = tf.layers.conv2d_transpose(x1, 256, kernel, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256
        
        x3 = tf.layers.conv2d_transpose(x2, 128, kernel, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 14x14x128
        
        logits = tf.layers.conv2d_transpose(
            x3, out_channel_dim, kernel, strides=1, padding='same')
        # 28x28x3
        
        out = tf.tanh(logits)
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [59]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    gen_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(gen_model, reuse=True)
    
    ones_like_real = tf.ones_like(d_model_real)
    one_sided_smooth_labels = tf.multiply(
        ones_like_real,
        tf.random_uniform((1,), minval=0.8, maxval=1.)
    )

    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_real, labels=one_sided_smooth_labels
        )
    )
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)
        )
    )
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake, labels=tf.ones_like(d_model_fake)
        )
    )
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [60]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [61]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [62]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    steps=0
    
    # TODO: Build Model
    image_channels = 3 if data_image_mode == 'RGB' else 1
    image_height, image_width = data_shape[1], data_shape[2]
    real_inputs, z_inputs, lrate = model_inputs(
        image_width, image_height, image_channels, z_dim)
        
    d_loss, g_loss = model_loss(real_inputs, z_inputs, image_channels)
    
    d_opt, g_opt = model_opt(d_loss, g_loss, lrate, beta1)
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                steps += 1
                batch_images = 2 * batch_images
                
                batch_z = np.random.uniform(-1 ,1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={
                    real_inputs: batch_images,
                    z_inputs: batch_z,
                    lrate: learning_rate
                })
                
                # Double the number of trains to generator
                _ = sess.run(g_opt, feed_dict={
                    z_inputs: batch_z,
                    real_inputs: batch_images,
                    lrate: learning_rate
                })
                
                
                if steps % 10 == 0:
                    # At the end of every 10 epochs, get the losses and print them out
                    train_loss_d = d_loss.eval({z_inputs: batch_z, real_inputs: batch_images})
                    train_loss_g = g_loss.eval({z_inputs: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g),
                          "Sum Loss: {:.4f}".format(train_loss_g+train_loss_d))
                
                if steps % 100 == 0:
                    show_generator_output(
                        sess,
                        25,
                        z_inputs,
                        image_channels,
                        data_image_mode
                    )
                  
        show_generator_output(sess, 25, z_inputs, image_channels, data_image_mode)

                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
 
In [63]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.2662... Generator Loss: 0.6247 Sum Loss: 1.8909
Epoch 1/2... Discriminator Loss: 0.6535... Generator Loss: 1.8987 Sum Loss: 2.5523
Epoch 1/2... Discriminator Loss: 0.6847... Generator Loss: 2.0016 Sum Loss: 2.6862
Epoch 1/2... Discriminator Loss: 0.4062... Generator Loss: 2.7437 Sum Loss: 3.1499
Epoch 1/2... Discriminator Loss: 0.6882... Generator Loss: 1.7130 Sum Loss: 2.4012
Epoch 1/2... Discriminator Loss: 0.4118... Generator Loss: 2.6047 Sum Loss: 3.0166
Epoch 1/2... Discriminator Loss: 0.9908... Generator Loss: 0.9526 Sum Loss: 1.9434
Epoch 1/2... Discriminator Loss: 0.6842... Generator Loss: 1.8759 Sum Loss: 2.5600
Epoch 1/2... Discriminator Loss: 0.8257... Generator Loss: 1.1272 Sum Loss: 1.9529
Epoch 1/2... Discriminator Loss: 0.9478... Generator Loss: 1.4784 Sum Loss: 2.4262
Epoch 1/2... Discriminator Loss: 0.9349... Generator Loss: 1.3748 Sum Loss: 2.3096
Epoch 1/2... Discriminator Loss: 0.8804... Generator Loss: 1.2494 Sum Loss: 2.1298
Epoch 1/2... Discriminator Loss: 1.4179... Generator Loss: 0.9524 Sum Loss: 2.3703
Epoch 1/2... Discriminator Loss: 0.8760... Generator Loss: 1.1457 Sum Loss: 2.0217
Epoch 1/2... Discriminator Loss: 1.2815... Generator Loss: 0.7510 Sum Loss: 2.0325
Epoch 1/2... Discriminator Loss: 0.9846... Generator Loss: 1.0709 Sum Loss: 2.0555
Epoch 1/2... Discriminator Loss: 0.9653... Generator Loss: 1.4256 Sum Loss: 2.3909
Epoch 1/2... Discriminator Loss: 1.0540... Generator Loss: 0.8472 Sum Loss: 1.9013
Epoch 1/2... Discriminator Loss: 1.1446... Generator Loss: 0.8711 Sum Loss: 2.0156
Epoch 1/2... Discriminator Loss: 1.2163... Generator Loss: 0.7785 Sum Loss: 1.9948
Epoch 1/2... Discriminator Loss: 1.2503... Generator Loss: 0.9842 Sum Loss: 2.2346
Epoch 1/2... Discriminator Loss: 1.3650... Generator Loss: 0.6476 Sum Loss: 2.0126
Epoch 1/2... Discriminator Loss: 1.3597... Generator Loss: 0.7854 Sum Loss: 2.1451
Epoch 1/2... Discriminator Loss: 1.5705... Generator Loss: 0.6953 Sum Loss: 2.2658
Epoch 1/2... Discriminator Loss: 1.5217... Generator Loss: 0.7067 Sum Loss: 2.2284
Epoch 1/2... Discriminator Loss: 1.4001... Generator Loss: 0.8438 Sum Loss: 2.2439
Epoch 1/2... Discriminator Loss: 1.4907... Generator Loss: 0.7303 Sum Loss: 2.2210
Epoch 1/2... Discriminator Loss: 1.4837... Generator Loss: 1.0100 Sum Loss: 2.4936
Epoch 1/2... Discriminator Loss: 1.4286... Generator Loss: 0.6298 Sum Loss: 2.0584
Epoch 1/2... Discriminator Loss: 1.5392... Generator Loss: 0.9648 Sum Loss: 2.5040
Epoch 1/2... Discriminator Loss: 1.5542... Generator Loss: 0.5061 Sum Loss: 2.0603
Epoch 1/2... Discriminator Loss: 1.4058... Generator Loss: 0.6348 Sum Loss: 2.0406
Epoch 1/2... Discriminator Loss: 1.5972... Generator Loss: 0.5250 Sum Loss: 2.1222
Epoch 1/2... Discriminator Loss: 1.3825... Generator Loss: 0.7317 Sum Loss: 2.1142
Epoch 1/2... Discriminator Loss: 1.5078... Generator Loss: 0.5579 Sum Loss: 2.0657
Epoch 1/2... Discriminator Loss: 1.4497... Generator Loss: 0.7902 Sum Loss: 2.2399
Epoch 1/2... Discriminator Loss: 1.3808... Generator Loss: 0.7413 Sum Loss: 2.1221
Epoch 1/2... Discriminator Loss: 1.2342... Generator Loss: 0.7537 Sum Loss: 1.9879
Epoch 1/2... Discriminator Loss: 1.1965... Generator Loss: 1.0285 Sum Loss: 2.2250
Epoch 1/2... Discriminator Loss: 1.1304... Generator Loss: 1.0018 Sum Loss: 2.1321
Epoch 1/2... Discriminator Loss: 1.5857... Generator Loss: 0.6203 Sum Loss: 2.2059
Epoch 1/2... Discriminator Loss: 1.1274... Generator Loss: 1.0369 Sum Loss: 2.1644
Epoch 1/2... Discriminator Loss: 1.1003... Generator Loss: 1.2751 Sum Loss: 2.3754
Epoch 1/2... Discriminator Loss: 1.1017... Generator Loss: 1.4456 Sum Loss: 2.5473
Epoch 1/2... Discriminator Loss: 1.2788... Generator Loss: 0.9697 Sum Loss: 2.2485
Epoch 1/2... Discriminator Loss: 1.4758... Generator Loss: 0.6261 Sum Loss: 2.1019
Epoch 1/2... Discriminator Loss: 1.2501... Generator Loss: 0.7670 Sum Loss: 2.0171
Epoch 1/2... Discriminator Loss: 1.3465... Generator Loss: 0.7059 Sum Loss: 2.0524
Epoch 1/2... Discriminator Loss: 1.0227... Generator Loss: 1.3132 Sum Loss: 2.3359
Epoch 1/2... Discriminator Loss: 1.2404... Generator Loss: 0.7988 Sum Loss: 2.0393
Epoch 1/2... Discriminator Loss: 1.2792... Generator Loss: 0.9643 Sum Loss: 2.2435
Epoch 1/2... Discriminator Loss: 1.3053... Generator Loss: 0.9316 Sum Loss: 2.2369
Epoch 1/2... Discriminator Loss: 0.9939... Generator Loss: 1.3059 Sum Loss: 2.2998
Epoch 1/2... Discriminator Loss: 1.0684... Generator Loss: 1.3280 Sum Loss: 2.3965
Epoch 1/2... Discriminator Loss: 1.1425... Generator Loss: 1.2511 Sum Loss: 2.3936
Epoch 1/2... Discriminator Loss: 1.1559... Generator Loss: 0.8937 Sum Loss: 2.0496
Epoch 1/2... Discriminator Loss: 1.0122... Generator Loss: 0.9876 Sum Loss: 1.9998
Epoch 1/2... Discriminator Loss: 1.3245... Generator Loss: 1.1346 Sum Loss: 2.4592
Epoch 1/2... Discriminator Loss: 1.1492... Generator Loss: 0.9637 Sum Loss: 2.1129
Epoch 1/2... Discriminator Loss: 1.4621... Generator Loss: 0.6956 Sum Loss: 2.1578
Epoch 1/2... Discriminator Loss: 0.9956... Generator Loss: 1.3368 Sum Loss: 2.3324
Epoch 1/2... Discriminator Loss: 1.2966... Generator Loss: 0.8743 Sum Loss: 2.1710
Epoch 1/2... Discriminator Loss: 1.0613... Generator Loss: 1.0550 Sum Loss: 2.1163
Epoch 1/2... Discriminator Loss: 0.9887... Generator Loss: 1.6536 Sum Loss: 2.6423
Epoch 1/2... Discriminator Loss: 1.4785... Generator Loss: 0.6916 Sum Loss: 2.1701
Epoch 1/2... Discriminator Loss: 1.5109... Generator Loss: 0.5544 Sum Loss: 2.0652
Epoch 1/2... Discriminator Loss: 1.3680... Generator Loss: 0.7036 Sum Loss: 2.0716
Epoch 1/2... Discriminator Loss: 1.3692... Generator Loss: 0.8222 Sum Loss: 2.1914
Epoch 1/2... Discriminator Loss: 1.0402... Generator Loss: 1.5174 Sum Loss: 2.5575
Epoch 1/2... Discriminator Loss: 1.2075... Generator Loss: 1.4566 Sum Loss: 2.6642
Epoch 1/2... Discriminator Loss: 1.5021... Generator Loss: 0.6002 Sum Loss: 2.1023
Epoch 1/2... Discriminator Loss: 1.2973... Generator Loss: 0.7774 Sum Loss: 2.0747
Epoch 1/2... Discriminator Loss: 1.2885... Generator Loss: 0.9774 Sum Loss: 2.2660
Epoch 1/2... Discriminator Loss: 1.4054... Generator Loss: 0.6814 Sum Loss: 2.0869
Epoch 1/2... Discriminator Loss: 1.3864... Generator Loss: 0.7421 Sum Loss: 2.1285
Epoch 1/2... Discriminator Loss: 1.3764... Generator Loss: 0.7322 Sum Loss: 2.1087
Epoch 1/2... Discriminator Loss: 1.4190... Generator Loss: 0.9551 Sum Loss: 2.3740
Epoch 1/2... Discriminator Loss: 1.3980... Generator Loss: 0.6727 Sum Loss: 2.0707
Epoch 1/2... Discriminator Loss: 1.4349... Generator Loss: 0.7485 Sum Loss: 2.1834
Epoch 1/2... Discriminator Loss: 1.4253... Generator Loss: 0.8417 Sum Loss: 2.2670
Epoch 1/2... Discriminator Loss: 1.3473... Generator Loss: 0.8840 Sum Loss: 2.2313
Epoch 1/2... Discriminator Loss: 1.4006... Generator Loss: 0.7886 Sum Loss: 2.1892
Epoch 1/2... Discriminator Loss: 1.3686... Generator Loss: 0.9248 Sum Loss: 2.2934
Epoch 1/2... Discriminator Loss: 1.3239... Generator Loss: 0.8765 Sum Loss: 2.2004
Epoch 1/2... Discriminator Loss: 1.4305... Generator Loss: 0.6859 Sum Loss: 2.1163
Epoch 1/2... Discriminator Loss: 1.3663... Generator Loss: 0.9549 Sum Loss: 2.3212
Epoch 1/2... Discriminator Loss: 1.3243... Generator Loss: 0.8581 Sum Loss: 2.1824
Epoch 1/2... Discriminator Loss: 1.4018... Generator Loss: 0.7150 Sum Loss: 2.1168
Epoch 1/2... Discriminator Loss: 1.4111... Generator Loss: 0.6590 Sum Loss: 2.0701
Epoch 1/2... Discriminator Loss: 1.3967... Generator Loss: 0.8326 Sum Loss: 2.2293
Epoch 1/2... Discriminator Loss: 1.4526... Generator Loss: 0.7415 Sum Loss: 2.1941
Epoch 1/2... Discriminator Loss: 1.4197... Generator Loss: 0.6406 Sum Loss: 2.0603
Epoch 1/2... Discriminator Loss: 1.4716... Generator Loss: 0.5704 Sum Loss: 2.0419
Epoch 2/2... Discriminator Loss: 1.4455... Generator Loss: 0.6290 Sum Loss: 2.0745
Epoch 2/2... Discriminator Loss: 1.4304... Generator Loss: 0.8273 Sum Loss: 2.2577
Epoch 2/2... Discriminator Loss: 1.3988... Generator Loss: 0.7085 Sum Loss: 2.1072
Epoch 2/2... Discriminator Loss: 1.3575... Generator Loss: 0.7634 Sum Loss: 2.1209
Epoch 2/2... Discriminator Loss: 1.3336... Generator Loss: 0.7638 Sum Loss: 2.0974
Epoch 2/2... Discriminator Loss: 1.4220... Generator Loss: 0.8536 Sum Loss: 2.2755
Epoch 2/2... Discriminator Loss: 1.4274... Generator Loss: 0.9007 Sum Loss: 2.3281
Epoch 2/2... Discriminator Loss: 1.3612... Generator Loss: 0.9228 Sum Loss: 2.2840
Epoch 2/2... Discriminator Loss: 1.3826... Generator Loss: 0.7254 Sum Loss: 2.1081
Epoch 2/2... Discriminator Loss: 1.3393... Generator Loss: 0.9027 Sum Loss: 2.2420
Epoch 2/2... Discriminator Loss: 1.4592... Generator Loss: 0.7070 Sum Loss: 2.1662
Epoch 2/2... Discriminator Loss: 1.3826... Generator Loss: 0.7997 Sum Loss: 2.1823
Epoch 2/2... Discriminator Loss: 1.3505... Generator Loss: 0.8827 Sum Loss: 2.2332
Epoch 2/2... Discriminator Loss: 1.3534... Generator Loss: 0.7189 Sum Loss: 2.0723
Epoch 2/2... Discriminator Loss: 1.3946... Generator Loss: 0.7081 Sum Loss: 2.1027
Epoch 2/2... Discriminator Loss: 1.4059... Generator Loss: 0.8148 Sum Loss: 2.2207
Epoch 2/2... Discriminator Loss: 1.3788... Generator Loss: 0.8141 Sum Loss: 2.1929
Epoch 2/2... Discriminator Loss: 1.3518... Generator Loss: 0.8852 Sum Loss: 2.2370
Epoch 2/2... Discriminator Loss: 1.3853... Generator Loss: 0.8105 Sum Loss: 2.1958
Epoch 2/2... Discriminator Loss: 1.4084... Generator Loss: 0.8268 Sum Loss: 2.2352
Epoch 2/2... Discriminator Loss: 1.3272... Generator Loss: 0.8002 Sum Loss: 2.1274
Epoch 2/2... Discriminator Loss: 1.4229... Generator Loss: 0.7370 Sum Loss: 2.1599
Epoch 2/2... Discriminator Loss: 1.4230... Generator Loss: 0.9041 Sum Loss: 2.3271
Epoch 2/2... Discriminator Loss: 1.3689... Generator Loss: 0.7286 Sum Loss: 2.0976
Epoch 2/2... Discriminator Loss: 1.3653... Generator Loss: 0.7814 Sum Loss: 2.1467
Epoch 2/2... Discriminator Loss: 1.4275... Generator Loss: 0.6769 Sum Loss: 2.1044
Epoch 2/2... Discriminator Loss: 1.3916... Generator Loss: 0.7551 Sum Loss: 2.1467
Epoch 2/2... Discriminator Loss: 1.3485... Generator Loss: 0.8140 Sum Loss: 2.1624
Epoch 2/2... Discriminator Loss: 1.3552... Generator Loss: 0.6680 Sum Loss: 2.0232
Epoch 2/2... Discriminator Loss: 1.4141... Generator Loss: 0.7287 Sum Loss: 2.1428
Epoch 2/2... Discriminator Loss: 1.4665... Generator Loss: 0.9224 Sum Loss: 2.3889
Epoch 2/2... Discriminator Loss: 1.3956... Generator Loss: 0.6588 Sum Loss: 2.0544
Epoch 2/2... Discriminator Loss: 1.3820... Generator Loss: 0.8140 Sum Loss: 2.1960
Epoch 2/2... Discriminator Loss: 1.4132... Generator Loss: 0.7232 Sum Loss: 2.1365
Epoch 2/2... Discriminator Loss: 1.3779... Generator Loss: 0.7718 Sum Loss: 2.1497
Epoch 2/2... Discriminator Loss: 1.3831... Generator Loss: 0.8599 Sum Loss: 2.2430
Epoch 2/2... Discriminator Loss: 1.4143... Generator Loss: 0.6316 Sum Loss: 2.0458
Epoch 2/2... Discriminator Loss: 1.3805... Generator Loss: 0.7530 Sum Loss: 2.1335
Epoch 2/2... Discriminator Loss: 1.3756... Generator Loss: 0.7072 Sum Loss: 2.0828
Epoch 2/2... Discriminator Loss: 1.4340... Generator Loss: 0.6401 Sum Loss: 2.0740
Epoch 2/2... Discriminator Loss: 1.4018... Generator Loss: 0.7685 Sum Loss: 2.1703
Epoch 2/2... Discriminator Loss: 1.3708... Generator Loss: 0.7779 Sum Loss: 2.1487
Epoch 2/2... Discriminator Loss: 1.4020... Generator Loss: 0.7106 Sum Loss: 2.1126
Epoch 2/2... Discriminator Loss: 1.3796... Generator Loss: 0.9137 Sum Loss: 2.2933
Epoch 2/2... Discriminator Loss: 1.3627... Generator Loss: 0.7330 Sum Loss: 2.0957
Epoch 2/2... Discriminator Loss: 1.3783... Generator Loss: 0.7290 Sum Loss: 2.1074
Epoch 2/2... Discriminator Loss: 1.3726... Generator Loss: 0.7584 Sum Loss: 2.1310
Epoch 2/2... Discriminator Loss: 1.3973... Generator Loss: 0.7703 Sum Loss: 2.1676
Epoch 2/2... Discriminator Loss: 1.3932... Generator Loss: 0.6911 Sum Loss: 2.0843
Epoch 2/2... Discriminator Loss: 1.3998... Generator Loss: 0.8745 Sum Loss: 2.2743
Epoch 2/2... Discriminator Loss: 1.3579... Generator Loss: 0.7197 Sum Loss: 2.0776
Epoch 2/2... Discriminator Loss: 1.3806... Generator Loss: 0.7477 Sum Loss: 2.1282
Epoch 2/2... Discriminator Loss: 1.4024... Generator Loss: 0.9020 Sum Loss: 2.3044
Epoch 2/2... Discriminator Loss: 1.3483... Generator Loss: 0.7345 Sum Loss: 2.0827
Epoch 2/2... Discriminator Loss: 1.4005... Generator Loss: 0.8049 Sum Loss: 2.2054
Epoch 2/2... Discriminator Loss: 1.4083... Generator Loss: 0.7456 Sum Loss: 2.1539
Epoch 2/2... Discriminator Loss: 1.3603... Generator Loss: 0.7199 Sum Loss: 2.0803
Epoch 2/2... Discriminator Loss: 1.3711... Generator Loss: 0.7577 Sum Loss: 2.1289
Epoch 2/2... Discriminator Loss: 1.3734... Generator Loss: 0.7578 Sum Loss: 2.1312
Epoch 2/2... Discriminator Loss: 1.4089... Generator Loss: 0.7175 Sum Loss: 2.1264
Epoch 2/2... Discriminator Loss: 1.3873... Generator Loss: 0.8578 Sum Loss: 2.2451
Epoch 2/2... Discriminator Loss: 1.3683... Generator Loss: 0.7588 Sum Loss: 2.1271
Epoch 2/2... Discriminator Loss: 1.3814... Generator Loss: 0.7640 Sum Loss: 2.1454
Epoch 2/2... Discriminator Loss: 1.3793... Generator Loss: 0.7791 Sum Loss: 2.1584
Epoch 2/2... Discriminator Loss: 1.4273... Generator Loss: 0.7691 Sum Loss: 2.1965
Epoch 2/2... Discriminator Loss: 1.4000... Generator Loss: 0.6921 Sum Loss: 2.0921
Epoch 2/2... Discriminator Loss: 1.4014... Generator Loss: 0.6178 Sum Loss: 2.0193
Epoch 2/2... Discriminator Loss: 1.3937... Generator Loss: 0.6392 Sum Loss: 2.0329
Epoch 2/2... Discriminator Loss: 1.4132... Generator Loss: 0.7436 Sum Loss: 2.1568
Epoch 2/2... Discriminator Loss: 1.3679... Generator Loss: 0.8211 Sum Loss: 2.1889
Epoch 2/2... Discriminator Loss: 1.4055... Generator Loss: 0.7505 Sum Loss: 2.1561
Epoch 2/2... Discriminator Loss: 1.3956... Generator Loss: 0.7487 Sum Loss: 2.1444
Epoch 2/2... Discriminator Loss: 1.3913... Generator Loss: 0.6939 Sum Loss: 2.0853
Epoch 2/2... Discriminator Loss: 1.3883... Generator Loss: 0.6771 Sum Loss: 2.0655
Epoch 2/2... Discriminator Loss: 1.3737... Generator Loss: 0.9307 Sum Loss: 2.3045
Epoch 2/2... Discriminator Loss: 1.3551... Generator Loss: 0.8558 Sum Loss: 2.2109
Epoch 2/2... Discriminator Loss: 1.3554... Generator Loss: 0.6251 Sum Loss: 1.9805
Epoch 2/2... Discriminator Loss: 1.3865... Generator Loss: 0.7813 Sum Loss: 2.1678
Epoch 2/2... Discriminator Loss: 1.4353... Generator Loss: 0.7886 Sum Loss: 2.2239
Epoch 2/2... Discriminator Loss: 1.4039... Generator Loss: 0.8218 Sum Loss: 2.2257
Epoch 2/2... Discriminator Loss: 1.4103... Generator Loss: 0.8701 Sum Loss: 2.2804
Epoch 2/2... Discriminator Loss: 1.3758... Generator Loss: 0.8611 Sum Loss: 2.2369
Epoch 2/2... Discriminator Loss: 1.3915... Generator Loss: 0.7655 Sum Loss: 2.1569
Epoch 2/2... Discriminator Loss: 1.3384... Generator Loss: 0.7955 Sum Loss: 2.1340
Epoch 2/2... Discriminator Loss: 1.3970... Generator Loss: 0.8052 Sum Loss: 2.2022
Epoch 2/2... Discriminator Loss: 1.4008... Generator Loss: 0.8549 Sum Loss: 2.2557
Epoch 2/2... Discriminator Loss: 1.4018... Generator Loss: 0.9089 Sum Loss: 2.3107
Epoch 2/2... Discriminator Loss: 1.3749... Generator Loss: 0.7004 Sum Loss: 2.0753
Epoch 2/2... Discriminator Loss: 1.4318... Generator Loss: 0.8438 Sum Loss: 2.2756
Epoch 2/2... Discriminator Loss: 1.4330... Generator Loss: 0.9766 Sum Loss: 2.4096
Epoch 2/2... Discriminator Loss: 1.4050... Generator Loss: 0.6951 Sum Loss: 2.1000
Epoch 2/2... Discriminator Loss: 1.3918... Generator Loss: 0.6136 Sum Loss: 2.0054
Epoch 2/2... Discriminator Loss: 1.3994... Generator Loss: 0.6704 Sum Loss: 2.0698
Epoch 2/2... Discriminator Loss: 1.4560... Generator Loss: 0.7794 Sum Loss: 2.2354

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [64]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 4.7231... Generator Loss: 0.0168 Sum Loss: 4.7399
Epoch 1/1... Discriminator Loss: 3.5888... Generator Loss: 0.0564 Sum Loss: 3.6452
Epoch 1/1... Discriminator Loss: 3.9233... Generator Loss: 0.0383 Sum Loss: 3.9616
Epoch 1/1... Discriminator Loss: 2.1946... Generator Loss: 0.2919 Sum Loss: 2.4865
Epoch 1/1... Discriminator Loss: 2.5241... Generator Loss: 0.3029 Sum Loss: 2.8270
Epoch 1/1... Discriminator Loss: 2.2541... Generator Loss: 0.4104 Sum Loss: 2.6645
Epoch 1/1... Discriminator Loss: 1.8206... Generator Loss: 0.7778 Sum Loss: 2.5983
Epoch 1/1... Discriminator Loss: 2.2448... Generator Loss: 0.3436 Sum Loss: 2.5884
Epoch 1/1... Discriminator Loss: 1.7820... Generator Loss: 0.6844 Sum Loss: 2.4664
Epoch 1/1... Discriminator Loss: 2.1245... Generator Loss: 0.3728 Sum Loss: 2.4972
Epoch 1/1... Discriminator Loss: 1.7126... Generator Loss: 0.7354 Sum Loss: 2.4480
Epoch 1/1... Discriminator Loss: 1.5768... Generator Loss: 0.5244 Sum Loss: 2.1012
Epoch 1/1... Discriminator Loss: 1.1964... Generator Loss: 1.1087 Sum Loss: 2.3051
Epoch 1/1... Discriminator Loss: 1.6146... Generator Loss: 0.6820 Sum Loss: 2.2967
Epoch 1/1... Discriminator Loss: 1.2969... Generator Loss: 0.9078 Sum Loss: 2.2047
Epoch 1/1... Discriminator Loss: 1.4110... Generator Loss: 0.8031 Sum Loss: 2.2141
Epoch 1/1... Discriminator Loss: 1.4431... Generator Loss: 0.8974 Sum Loss: 2.3405
Epoch 1/1... Discriminator Loss: 1.8341... Generator Loss: 0.4274 Sum Loss: 2.2615
Epoch 1/1... Discriminator Loss: 0.9018... Generator Loss: 2.1455 Sum Loss: 3.0473
Epoch 1/1... Discriminator Loss: 1.2778... Generator Loss: 0.6829 Sum Loss: 1.9607
Epoch 1/1... Discriminator Loss: 0.9003... Generator Loss: 1.9067 Sum Loss: 2.8070
Epoch 1/1... Discriminator Loss: 1.1400... Generator Loss: 0.9823 Sum Loss: 2.1223
Epoch 1/1... Discriminator Loss: 1.0532... Generator Loss: 1.6210 Sum Loss: 2.6742
Epoch 1/1... Discriminator Loss: 0.9452... Generator Loss: 1.4995 Sum Loss: 2.4448
Epoch 1/1... Discriminator Loss: 0.8857... Generator Loss: 1.5337 Sum Loss: 2.4194
Epoch 1/1... Discriminator Loss: 1.1132... Generator Loss: 0.9270 Sum Loss: 2.0403
Epoch 1/1... Discriminator Loss: 1.0645... Generator Loss: 2.4745 Sum Loss: 3.5390
Epoch 1/1... Discriminator Loss: 0.6847... Generator Loss: 2.0340 Sum Loss: 2.7188
Epoch 1/1... Discriminator Loss: 0.8099... Generator Loss: 1.8369 Sum Loss: 2.6469
Epoch 1/1... Discriminator Loss: 0.7078... Generator Loss: 2.4804 Sum Loss: 3.1882
Epoch 1/1... Discriminator Loss: 0.5763... Generator Loss: 2.5801 Sum Loss: 3.1563
Epoch 1/1... Discriminator Loss: 0.9361... Generator Loss: 1.6276 Sum Loss: 2.5637
Epoch 1/1... Discriminator Loss: 0.6110... Generator Loss: 2.2724 Sum Loss: 2.8833
Epoch 1/1... Discriminator Loss: 0.5971... Generator Loss: 2.4544 Sum Loss: 3.0514
Epoch 1/1... Discriminator Loss: 1.2302... Generator Loss: 0.8305 Sum Loss: 2.0607
Epoch 1/1... Discriminator Loss: 0.8185... Generator Loss: 2.3568 Sum Loss: 3.1753
Epoch 1/1... Discriminator Loss: 0.5887... Generator Loss: 2.9628 Sum Loss: 3.5515
Epoch 1/1... Discriminator Loss: 1.5837... Generator Loss: 0.8350 Sum Loss: 2.4187
Epoch 1/1... Discriminator Loss: 0.5119... Generator Loss: 4.2505 Sum Loss: 4.7625
Epoch 1/1... Discriminator Loss: 0.6968... Generator Loss: 1.5802 Sum Loss: 2.2771
Epoch 1/1... Discriminator Loss: 1.2536... Generator Loss: 1.3495 Sum Loss: 2.6031
Epoch 1/1... Discriminator Loss: 0.6577... Generator Loss: 2.2449 Sum Loss: 2.9025
Epoch 1/1... Discriminator Loss: 0.5319... Generator Loss: 2.9122 Sum Loss: 3.4442
Epoch 1/1... Discriminator Loss: 1.7712... Generator Loss: 0.3318 Sum Loss: 2.1030
Epoch 1/1... Discriminator Loss: 0.5519... Generator Loss: 2.0986 Sum Loss: 2.6505
Epoch 1/1... Discriminator Loss: 0.8507... Generator Loss: 1.4812 Sum Loss: 2.3319
Epoch 1/1... Discriminator Loss: 1.2155... Generator Loss: 0.6182 Sum Loss: 1.8337
Epoch 1/1... Discriminator Loss: 1.2816... Generator Loss: 0.6250 Sum Loss: 1.9065
Epoch 1/1... Discriminator Loss: 0.6478... Generator Loss: 1.8140 Sum Loss: 2.4618
Epoch 1/1... Discriminator Loss: 0.9667... Generator Loss: 1.6601 Sum Loss: 2.6268
Epoch 1/1... Discriminator Loss: 0.9877... Generator Loss: 2.0673 Sum Loss: 3.0550
Epoch 1/1... Discriminator Loss: 1.2756... Generator Loss: 1.7261 Sum Loss: 3.0017
Epoch 1/1... Discriminator Loss: 0.6611... Generator Loss: 3.0053 Sum Loss: 3.6665
Epoch 1/1... Discriminator Loss: 0.9943... Generator Loss: 2.2018 Sum Loss: 3.1962
Epoch 1/1... Discriminator Loss: 0.5217... Generator Loss: 2.1764 Sum Loss: 2.6981
Epoch 1/1... Discriminator Loss: 0.9611... Generator Loss: 1.1510 Sum Loss: 2.1122
Epoch 1/1... Discriminator Loss: 1.0200... Generator Loss: 1.2246 Sum Loss: 2.2447
Epoch 1/1... Discriminator Loss: 0.8970... Generator Loss: 1.0789 Sum Loss: 1.9760
Epoch 1/1... Discriminator Loss: 0.9502... Generator Loss: 1.1598 Sum Loss: 2.1100
Epoch 1/1... Discriminator Loss: 0.8129... Generator Loss: 1.9061 Sum Loss: 2.7191
Epoch 1/1... Discriminator Loss: 1.2326... Generator Loss: 0.7878 Sum Loss: 2.0204
Epoch 1/1... Discriminator Loss: 0.8579... Generator Loss: 1.2319 Sum Loss: 2.0898
Epoch 1/1... Discriminator Loss: 0.7420... Generator Loss: 2.4613 Sum Loss: 3.2033
Epoch 1/1... Discriminator Loss: 1.2662... Generator Loss: 0.9487 Sum Loss: 2.2148
Epoch 1/1... Discriminator Loss: 1.0040... Generator Loss: 0.8309 Sum Loss: 1.8349
Epoch 1/1... Discriminator Loss: 0.6102... Generator Loss: 1.8276 Sum Loss: 2.4378
Epoch 1/1... Discriminator Loss: 0.6879... Generator Loss: 1.7932 Sum Loss: 2.4811
Epoch 1/1... Discriminator Loss: 1.8587... Generator Loss: 1.4055 Sum Loss: 3.2642
Epoch 1/1... Discriminator Loss: 1.2233... Generator Loss: 1.4074 Sum Loss: 2.6307
Epoch 1/1... Discriminator Loss: 1.4513... Generator Loss: 0.7381 Sum Loss: 2.1894
Epoch 1/1... Discriminator Loss: 0.7878... Generator Loss: 1.4866 Sum Loss: 2.2744
Epoch 1/1... Discriminator Loss: 1.0250... Generator Loss: 1.6248 Sum Loss: 2.6499
Epoch 1/1... Discriminator Loss: 1.9059... Generator Loss: 0.8775 Sum Loss: 2.7834
Epoch 1/1... Discriminator Loss: 0.8022... Generator Loss: 1.3667 Sum Loss: 2.1689
Epoch 1/1... Discriminator Loss: 1.3068... Generator Loss: 1.1710 Sum Loss: 2.4778
Epoch 1/1... Discriminator Loss: 0.7540... Generator Loss: 1.3580 Sum Loss: 2.1121
Epoch 1/1... Discriminator Loss: 0.8982... Generator Loss: 2.3963 Sum Loss: 3.2945
Epoch 1/1... Discriminator Loss: 1.3737... Generator Loss: 0.6638 Sum Loss: 2.0375
Epoch 1/1... Discriminator Loss: 1.2552... Generator Loss: 0.9958 Sum Loss: 2.2510
Epoch 1/1... Discriminator Loss: 1.0075... Generator Loss: 1.0284 Sum Loss: 2.0359
Epoch 1/1... Discriminator Loss: 0.8235... Generator Loss: 1.6777 Sum Loss: 2.5012
Epoch 1/1... Discriminator Loss: 1.4440... Generator Loss: 1.1528 Sum Loss: 2.5967
Epoch 1/1... Discriminator Loss: 0.9660... Generator Loss: 1.9651 Sum Loss: 2.9311
Epoch 1/1... Discriminator Loss: 1.4593... Generator Loss: 0.9290 Sum Loss: 2.3883
Epoch 1/1... Discriminator Loss: 1.6755... Generator Loss: 0.3871 Sum Loss: 2.0626
Epoch 1/1... Discriminator Loss: 0.7769... Generator Loss: 2.2121 Sum Loss: 2.9889
Epoch 1/1... Discriminator Loss: 1.6240... Generator Loss: 1.2313 Sum Loss: 2.8553
Epoch 1/1... Discriminator Loss: 0.9213... Generator Loss: 1.2184 Sum Loss: 2.1397
Epoch 1/1... Discriminator Loss: 1.4777... Generator Loss: 0.6632 Sum Loss: 2.1409
Epoch 1/1... Discriminator Loss: 1.2362... Generator Loss: 1.4462 Sum Loss: 2.6824
Epoch 1/1... Discriminator Loss: 1.4059... Generator Loss: 1.1970 Sum Loss: 2.6029
Epoch 1/1... Discriminator Loss: 1.5200... Generator Loss: 0.7041 Sum Loss: 2.2240
Epoch 1/1... Discriminator Loss: 1.3642... Generator Loss: 1.0218 Sum Loss: 2.3861
Epoch 1/1... Discriminator Loss: 1.3378... Generator Loss: 0.8657 Sum Loss: 2.2035
Epoch 1/1... Discriminator Loss: 1.4113... Generator Loss: 0.9363 Sum Loss: 2.3477
Epoch 1/1... Discriminator Loss: 1.2600... Generator Loss: 1.5560 Sum Loss: 2.8161
Epoch 1/1... Discriminator Loss: 0.9927... Generator Loss: 2.0950 Sum Loss: 3.0877
Epoch 1/1... Discriminator Loss: 1.7840... Generator Loss: 0.4684 Sum Loss: 2.2524
Epoch 1/1... Discriminator Loss: 1.3336... Generator Loss: 0.8905 Sum Loss: 2.2241
Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.8334 Sum Loss: 2.1970
Epoch 1/1... Discriminator Loss: 1.6312... Generator Loss: 0.5017 Sum Loss: 2.1330
Epoch 1/1... Discriminator Loss: 1.1985... Generator Loss: 1.2313 Sum Loss: 2.4298
Epoch 1/1... Discriminator Loss: 1.4909... Generator Loss: 1.1407 Sum Loss: 2.6315
Epoch 1/1... Discriminator Loss: 1.4926... Generator Loss: 0.7294 Sum Loss: 2.2220
Epoch 1/1... Discriminator Loss: 1.4115... Generator Loss: 0.7475 Sum Loss: 2.1590
Epoch 1/1... Discriminator Loss: 1.3640... Generator Loss: 0.9691 Sum Loss: 2.3331
Epoch 1/1... Discriminator Loss: 1.2705... Generator Loss: 1.0654 Sum Loss: 2.3359
Epoch 1/1... Discriminator Loss: 1.3727... Generator Loss: 0.7793 Sum Loss: 2.1521
Epoch 1/1... Discriminator Loss: 1.5594... Generator Loss: 0.6078 Sum Loss: 2.1671
Epoch 1/1... Discriminator Loss: 1.1404... Generator Loss: 1.0911 Sum Loss: 2.2314
Epoch 1/1... Discriminator Loss: 1.3374... Generator Loss: 0.9682 Sum Loss: 2.3056
Epoch 1/1... Discriminator Loss: 1.3491... Generator Loss: 0.7646 Sum Loss: 2.1137
Epoch 1/1... Discriminator Loss: 1.3232... Generator Loss: 0.9286 Sum Loss: 2.2518
Epoch 1/1... Discriminator Loss: 1.5034... Generator Loss: 0.5127 Sum Loss: 2.0161
Epoch 1/1... Discriminator Loss: 1.4738... Generator Loss: 0.5301 Sum Loss: 2.0039
Epoch 1/1... Discriminator Loss: 1.4680... Generator Loss: 0.7407 Sum Loss: 2.2087
Epoch 1/1... Discriminator Loss: 1.2772... Generator Loss: 0.9784 Sum Loss: 2.2556
Epoch 1/1... Discriminator Loss: 1.2515... Generator Loss: 0.9116 Sum Loss: 2.1631
Epoch 1/1... Discriminator Loss: 1.3935... Generator Loss: 0.7991 Sum Loss: 2.1926
Epoch 1/1... Discriminator Loss: 1.5804... Generator Loss: 0.8130 Sum Loss: 2.3934
Epoch 1/1... Discriminator Loss: 1.5016... Generator Loss: 0.9280 Sum Loss: 2.4296
Epoch 1/1... Discriminator Loss: 1.2453... Generator Loss: 0.8072 Sum Loss: 2.0525
Epoch 1/1... Discriminator Loss: 1.4477... Generator Loss: 0.8159 Sum Loss: 2.2635
Epoch 1/1... Discriminator Loss: 1.1669... Generator Loss: 0.7684 Sum Loss: 1.9353
Epoch 1/1... Discriminator Loss: 1.4282... Generator Loss: 0.6135 Sum Loss: 2.0417
Epoch 1/1... Discriminator Loss: 1.5402... Generator Loss: 0.7638 Sum Loss: 2.3040
Epoch 1/1... Discriminator Loss: 1.2763... Generator Loss: 0.6872 Sum Loss: 1.9635
Epoch 1/1... Discriminator Loss: 1.5871... Generator Loss: 0.6214 Sum Loss: 2.2085
Epoch 1/1... Discriminator Loss: 1.3654... Generator Loss: 0.7181 Sum Loss: 2.0835
Epoch 1/1... Discriminator Loss: 1.3654... Generator Loss: 0.7603 Sum Loss: 2.1257
Epoch 1/1... Discriminator Loss: 1.3470... Generator Loss: 0.8807 Sum Loss: 2.2278
Epoch 1/1... Discriminator Loss: 1.2829... Generator Loss: 0.7449 Sum Loss: 2.0278
Epoch 1/1... Discriminator Loss: 1.4597... Generator Loss: 0.5954 Sum Loss: 2.0551
Epoch 1/1... Discriminator Loss: 1.4047... Generator Loss: 0.6974 Sum Loss: 2.1020
Epoch 1/1... Discriminator Loss: 1.4738... Generator Loss: 0.5939 Sum Loss: 2.0677
Epoch 1/1... Discriminator Loss: 1.4481... Generator Loss: 0.6383 Sum Loss: 2.0863
Epoch 1/1... Discriminator Loss: 1.3346... Generator Loss: 0.8790 Sum Loss: 2.2137
Epoch 1/1... Discriminator Loss: 1.4166... Generator Loss: 0.7129 Sum Loss: 2.1295
Epoch 1/1... Discriminator Loss: 1.5012... Generator Loss: 0.6418 Sum Loss: 2.1430
Epoch 1/1... Discriminator Loss: 1.3703... Generator Loss: 0.8598 Sum Loss: 2.2302
Epoch 1/1... Discriminator Loss: 1.4991... Generator Loss: 0.7453 Sum Loss: 2.2444
Epoch 1/1... Discriminator Loss: 1.1811... Generator Loss: 1.1573 Sum Loss: 2.3384
Epoch 1/1... Discriminator Loss: 1.3490... Generator Loss: 1.1643 Sum Loss: 2.5133
Epoch 1/1... Discriminator Loss: 1.3446... Generator Loss: 0.7005 Sum Loss: 2.0451
Epoch 1/1... Discriminator Loss: 1.1711... Generator Loss: 1.0738 Sum Loss: 2.2449
Epoch 1/1... Discriminator Loss: 0.9140... Generator Loss: 1.5881 Sum Loss: 2.5020
Epoch 1/1... Discriminator Loss: 1.4060... Generator Loss: 0.6832 Sum Loss: 2.0892
Epoch 1/1... Discriminator Loss: 1.6104... Generator Loss: 0.6368 Sum Loss: 2.2472
Epoch 1/1... Discriminator Loss: 1.4841... Generator Loss: 0.8307 Sum Loss: 2.3148
Epoch 1/1... Discriminator Loss: 1.2608... Generator Loss: 0.9543 Sum Loss: 2.2151
Epoch 1/1... Discriminator Loss: 1.3608... Generator Loss: 0.6765 Sum Loss: 2.0373
Epoch 1/1... Discriminator Loss: 1.2727... Generator Loss: 0.8041 Sum Loss: 2.0767
Epoch 1/1... Discriminator Loss: 1.3265... Generator Loss: 0.9523 Sum Loss: 2.2788
Epoch 1/1... Discriminator Loss: 1.1531... Generator Loss: 0.9350 Sum Loss: 2.0881
Epoch 1/1... Discriminator Loss: 1.3321... Generator Loss: 0.7904 Sum Loss: 2.1225
Epoch 1/1... Discriminator Loss: 1.2705... Generator Loss: 0.7481 Sum Loss: 2.0186
Epoch 1/1... Discriminator Loss: 1.2213... Generator Loss: 0.8495 Sum Loss: 2.0708
Epoch 1/1... Discriminator Loss: 1.3333... Generator Loss: 0.9632 Sum Loss: 2.2965
Epoch 1/1... Discriminator Loss: 1.4162... Generator Loss: 0.5911 Sum Loss: 2.0073
Epoch 1/1... Discriminator Loss: 1.2045... Generator Loss: 1.2290 Sum Loss: 2.4335
Epoch 1/1... Discriminator Loss: 1.2939... Generator Loss: 0.6783 Sum Loss: 1.9723
Epoch 1/1... Discriminator Loss: 1.3477... Generator Loss: 1.1592 Sum Loss: 2.5069
Epoch 1/1... Discriminator Loss: 1.3073... Generator Loss: 0.7666 Sum Loss: 2.0739
Epoch 1/1... Discriminator Loss: 1.3965... Generator Loss: 0.8236 Sum Loss: 2.2201
Epoch 1/1... Discriminator Loss: 1.3550... Generator Loss: 0.8505 Sum Loss: 2.2055
Epoch 1/1... Discriminator Loss: 1.3598... Generator Loss: 0.7588 Sum Loss: 2.1186
Epoch 1/1... Discriminator Loss: 1.3706... Generator Loss: 0.9381 Sum Loss: 2.3086
Epoch 1/1... Discriminator Loss: 1.4377... Generator Loss: 0.8350 Sum Loss: 2.2727
Epoch 1/1... Discriminator Loss: 1.3269... Generator Loss: 0.9373 Sum Loss: 2.2643
Epoch 1/1... Discriminator Loss: 1.3569... Generator Loss: 0.8053 Sum Loss: 2.1622
Epoch 1/1... Discriminator Loss: 1.2778... Generator Loss: 0.7674 Sum Loss: 2.0452
Epoch 1/1... Discriminator Loss: 1.3889... Generator Loss: 0.7842 Sum Loss: 2.1731
Epoch 1/1... Discriminator Loss: 1.3651... Generator Loss: 0.9085 Sum Loss: 2.2736
Epoch 1/1... Discriminator Loss: 1.3992... Generator Loss: 0.7719 Sum Loss: 2.1712
Epoch 1/1... Discriminator Loss: 1.4393... Generator Loss: 0.6683 Sum Loss: 2.1076
Epoch 1/1... Discriminator Loss: 1.3696... Generator Loss: 0.7451 Sum Loss: 2.1147
Epoch 1/1... Discriminator Loss: 1.5051... Generator Loss: 0.7076 Sum Loss: 2.2127
Epoch 1/1... Discriminator Loss: 1.3199... Generator Loss: 0.8189 Sum Loss: 2.1388
Epoch 1/1... Discriminator Loss: 1.4450... Generator Loss: 0.7995 Sum Loss: 2.2445
Epoch 1/1... Discriminator Loss: 1.4100... Generator Loss: 0.6907 Sum Loss: 2.1007
Epoch 1/1... Discriminator Loss: 1.3897... Generator Loss: 0.8899 Sum Loss: 2.2796
Epoch 1/1... Discriminator Loss: 1.4560... Generator Loss: 0.8777 Sum Loss: 2.3336
Epoch 1/1... Discriminator Loss: 1.3913... Generator Loss: 0.6734 Sum Loss: 2.0647
Epoch 1/1... Discriminator Loss: 1.4646... Generator Loss: 0.7448 Sum Loss: 2.2095
Epoch 1/1... Discriminator Loss: 1.2458... Generator Loss: 0.8347 Sum Loss: 2.0805
Epoch 1/1... Discriminator Loss: 1.3424... Generator Loss: 0.8673 Sum Loss: 2.2097
Epoch 1/1... Discriminator Loss: 1.4180... Generator Loss: 0.7466 Sum Loss: 2.1647
Epoch 1/1... Discriminator Loss: 1.4704... Generator Loss: 1.1651 Sum Loss: 2.6354
Epoch 1/1... Discriminator Loss: 0.9114... Generator Loss: 1.7602 Sum Loss: 2.6715
Epoch 1/1... Discriminator Loss: 1.3778... Generator Loss: 0.9888 Sum Loss: 2.3666
Epoch 1/1... Discriminator Loss: 1.3188... Generator Loss: 0.9373 Sum Loss: 2.2561
Epoch 1/1... Discriminator Loss: 1.3242... Generator Loss: 1.0195 Sum Loss: 2.3436
Epoch 1/1... Discriminator Loss: 1.4092... Generator Loss: 0.7519 Sum Loss: 2.1611
Epoch 1/1... Discriminator Loss: 1.4140... Generator Loss: 0.8079 Sum Loss: 2.2219
Epoch 1/1... Discriminator Loss: 0.8040... Generator Loss: 2.1836 Sum Loss: 2.9876
Epoch 1/1... Discriminator Loss: 1.5019... Generator Loss: 0.5662 Sum Loss: 2.0681
Epoch 1/1... Discriminator Loss: 1.3503... Generator Loss: 0.7436 Sum Loss: 2.0940
Epoch 1/1... Discriminator Loss: 1.1512... Generator Loss: 1.2157 Sum Loss: 2.3669
Epoch 1/1... Discriminator Loss: 1.3295... Generator Loss: 0.8028 Sum Loss: 2.1323
Epoch 1/1... Discriminator Loss: 1.1623... Generator Loss: 1.0712 Sum Loss: 2.2335
Epoch 1/1... Discriminator Loss: 1.3416... Generator Loss: 0.7821 Sum Loss: 2.1237
Epoch 1/1... Discriminator Loss: 1.6164... Generator Loss: 0.4945 Sum Loss: 2.1108
Epoch 1/1... Discriminator Loss: 1.4520... Generator Loss: 0.8654 Sum Loss: 2.3174
Epoch 1/1... Discriminator Loss: 1.4960... Generator Loss: 0.9160 Sum Loss: 2.4120
Epoch 1/1... Discriminator Loss: 1.3479... Generator Loss: 0.7480 Sum Loss: 2.0958
Epoch 1/1... Discriminator Loss: 1.4154... Generator Loss: 0.8156 Sum Loss: 2.2310
Epoch 1/1... Discriminator Loss: 1.3270... Generator Loss: 0.9818 Sum Loss: 2.3088
Epoch 1/1... Discriminator Loss: 1.4518... Generator Loss: 0.7503 Sum Loss: 2.2022
Epoch 1/1... Discriminator Loss: 1.4006... Generator Loss: 0.8757 Sum Loss: 2.2762
Epoch 1/1... Discriminator Loss: 1.3661... Generator Loss: 0.7675 Sum Loss: 2.1336
Epoch 1/1... Discriminator Loss: 1.3954... Generator Loss: 0.5377 Sum Loss: 1.9332
Epoch 1/1... Discriminator Loss: 1.3448... Generator Loss: 0.7650 Sum Loss: 2.1098
Epoch 1/1... Discriminator Loss: 1.4902... Generator Loss: 0.6136 Sum Loss: 2.1038
Epoch 1/1... Discriminator Loss: 1.4102... Generator Loss: 1.0910 Sum Loss: 2.5011
Epoch 1/1... Discriminator Loss: 1.2903... Generator Loss: 0.9677 Sum Loss: 2.2580
Epoch 1/1... Discriminator Loss: 1.3991... Generator Loss: 0.7717 Sum Loss: 2.1707
Epoch 1/1... Discriminator Loss: 1.1016... Generator Loss: 1.5399 Sum Loss: 2.6415
Epoch 1/1... Discriminator Loss: 1.2265... Generator Loss: 0.9770 Sum Loss: 2.2035
Epoch 1/1... Discriminator Loss: 1.4214... Generator Loss: 0.8405 Sum Loss: 2.2619
Epoch 1/1... Discriminator Loss: 1.4286... Generator Loss: 0.8552 Sum Loss: 2.2838
Epoch 1/1... Discriminator Loss: 1.2846... Generator Loss: 0.9557 Sum Loss: 2.2403
Epoch 1/1... Discriminator Loss: 1.4411... Generator Loss: 0.6713 Sum Loss: 2.1124
Epoch 1/1... Discriminator Loss: 1.4447... Generator Loss: 0.8058 Sum Loss: 2.2505
Epoch 1/1... Discriminator Loss: 1.0863... Generator Loss: 1.7826 Sum Loss: 2.8689
Epoch 1/1... Discriminator Loss: 1.5584... Generator Loss: 0.8788 Sum Loss: 2.4372
Epoch 1/1... Discriminator Loss: 1.4036... Generator Loss: 0.8031 Sum Loss: 2.2067
Epoch 1/1... Discriminator Loss: 1.4289... Generator Loss: 0.7693 Sum Loss: 2.1982
Epoch 1/1... Discriminator Loss: 1.4017... Generator Loss: 0.7095 Sum Loss: 2.1111
Epoch 1/1... Discriminator Loss: 1.3896... Generator Loss: 0.8135 Sum Loss: 2.2031
Epoch 1/1... Discriminator Loss: 1.2863... Generator Loss: 0.8350 Sum Loss: 2.1213
Epoch 1/1... Discriminator Loss: 1.3912... Generator Loss: 0.9816 Sum Loss: 2.3728
Epoch 1/1... Discriminator Loss: 1.3233... Generator Loss: 0.7866 Sum Loss: 2.1099
Epoch 1/1... Discriminator Loss: 1.4415... Generator Loss: 0.9232 Sum Loss: 2.3647
Epoch 1/1... Discriminator Loss: 1.1657... Generator Loss: 1.0886 Sum Loss: 2.2543
Epoch 1/1... Discriminator Loss: 1.3790... Generator Loss: 0.6712 Sum Loss: 2.0502
Epoch 1/1... Discriminator Loss: 1.5123... Generator Loss: 0.9120 Sum Loss: 2.4243
Epoch 1/1... Discriminator Loss: 1.4706... Generator Loss: 0.6103 Sum Loss: 2.0810
Epoch 1/1... Discriminator Loss: 1.3915... Generator Loss: 0.7516 Sum Loss: 2.1431
Epoch 1/1... Discriminator Loss: 1.4842... Generator Loss: 0.8274 Sum Loss: 2.3116
Epoch 1/1... Discriminator Loss: 1.3672... Generator Loss: 0.7439 Sum Loss: 2.1111
Epoch 1/1... Discriminator Loss: 1.3878... Generator Loss: 0.7890 Sum Loss: 2.1768
Epoch 1/1... Discriminator Loss: 1.4579... Generator Loss: 0.6682 Sum Loss: 2.1262
Epoch 1/1... Discriminator Loss: 1.4039... Generator Loss: 0.7952 Sum Loss: 2.1991
Epoch 1/1... Discriminator Loss: 1.4266... Generator Loss: 0.6873 Sum Loss: 2.1139
Epoch 1/1... Discriminator Loss: 1.4214... Generator Loss: 0.6990 Sum Loss: 2.1204
Epoch 1/1... Discriminator Loss: 1.2984... Generator Loss: 0.9447 Sum Loss: 2.2431
Epoch 1/1... Discriminator Loss: 1.4604... Generator Loss: 0.6377 Sum Loss: 2.0981
Epoch 1/1... Discriminator Loss: 1.3607... Generator Loss: 0.9819 Sum Loss: 2.3426
Epoch 1/1... Discriminator Loss: 1.4340... Generator Loss: 0.7193 Sum Loss: 2.1533
Epoch 1/1... Discriminator Loss: 1.3983... Generator Loss: 0.7756 Sum Loss: 2.1739
Epoch 1/1... Discriminator Loss: 1.3778... Generator Loss: 0.7431 Sum Loss: 2.1209
Epoch 1/1... Discriminator Loss: 1.3880... Generator Loss: 0.6969 Sum Loss: 2.0849
Epoch 1/1... Discriminator Loss: 1.3269... Generator Loss: 0.8065 Sum Loss: 2.1333
Epoch 1/1... Discriminator Loss: 1.3567... Generator Loss: 0.8219 Sum Loss: 2.1786
Epoch 1/1... Discriminator Loss: 1.4234... Generator Loss: 0.8083 Sum Loss: 2.2316
Epoch 1/1... Discriminator Loss: 1.4232... Generator Loss: 0.8473 Sum Loss: 2.2705
Epoch 1/1... Discriminator Loss: 1.3431... Generator Loss: 0.9507 Sum Loss: 2.2937
Epoch 1/1... Discriminator Loss: 1.2935... Generator Loss: 1.0305 Sum Loss: 2.3240
Epoch 1/1... Discriminator Loss: 1.4427... Generator Loss: 0.7314 Sum Loss: 2.1741
Epoch 1/1... Discriminator Loss: 1.4844... Generator Loss: 1.0729 Sum Loss: 2.5573
Epoch 1/1... Discriminator Loss: 1.4577... Generator Loss: 0.6929 Sum Loss: 2.1506
Epoch 1/1... Discriminator Loss: 1.3723... Generator Loss: 0.9165 Sum Loss: 2.2889
Epoch 1/1... Discriminator Loss: 1.2993... Generator Loss: 0.6996 Sum Loss: 1.9989
Epoch 1/1... Discriminator Loss: 1.3988... Generator Loss: 0.8861 Sum Loss: 2.2850
Epoch 1/1... Discriminator Loss: 1.3629... Generator Loss: 0.8667 Sum Loss: 2.2296
Epoch 1/1... Discriminator Loss: 1.3765... Generator Loss: 0.8313 Sum Loss: 2.2077
Epoch 1/1... Discriminator Loss: 1.3504... Generator Loss: 0.8482 Sum Loss: 2.1986
Epoch 1/1... Discriminator Loss: 1.4505... Generator Loss: 0.7355 Sum Loss: 2.1860
Epoch 1/1... Discriminator Loss: 1.3099... Generator Loss: 0.8026 Sum Loss: 2.1125
Epoch 1/1... Discriminator Loss: 1.4577... Generator Loss: 0.5494 Sum Loss: 2.0071
Epoch 1/1... Discriminator Loss: 1.3621... Generator Loss: 0.8836 Sum Loss: 2.2457
Epoch 1/1... Discriminator Loss: 1.4097... Generator Loss: 0.7838 Sum Loss: 2.1935
Epoch 1/1... Discriminator Loss: 1.2106... Generator Loss: 0.9385 Sum Loss: 2.1491
Epoch 1/1... Discriminator Loss: 1.3328... Generator Loss: 0.7607 Sum Loss: 2.0935
Epoch 1/1... Discriminator Loss: 1.3430... Generator Loss: 0.8985 Sum Loss: 2.2415
Epoch 1/1... Discriminator Loss: 1.4435... Generator Loss: 0.6891 Sum Loss: 2.1325
Epoch 1/1... Discriminator Loss: 1.4554... Generator Loss: 0.8637 Sum Loss: 2.3191
Epoch 1/1... Discriminator Loss: 1.3697... Generator Loss: 1.0935 Sum Loss: 2.4631
Epoch 1/1... Discriminator Loss: 1.3714... Generator Loss: 0.7559 Sum Loss: 2.1273
Epoch 1/1... Discriminator Loss: 1.3551... Generator Loss: 0.7747 Sum Loss: 2.1299
Epoch 1/1... Discriminator Loss: 1.3648... Generator Loss: 0.6810 Sum Loss: 2.0458
Epoch 1/1... Discriminator Loss: 1.4250... Generator Loss: 1.1418 Sum Loss: 2.5668
Epoch 1/1... Discriminator Loss: 1.1931... Generator Loss: 1.0622 Sum Loss: 2.2553
Epoch 1/1... Discriminator Loss: 1.2881... Generator Loss: 0.9269 Sum Loss: 2.2150
Epoch 1/1... Discriminator Loss: 1.3264... Generator Loss: 0.8529 Sum Loss: 2.1793
Epoch 1/1... Discriminator Loss: 1.3041... Generator Loss: 0.8168 Sum Loss: 2.1209
Epoch 1/1... Discriminator Loss: 1.3977... Generator Loss: 0.8695 Sum Loss: 2.2672
Epoch 1/1... Discriminator Loss: 1.2804... Generator Loss: 0.8009 Sum Loss: 2.0813
Epoch 1/1... Discriminator Loss: 1.3702... Generator Loss: 0.7755 Sum Loss: 2.1457
Epoch 1/1... Discriminator Loss: 1.4045... Generator Loss: 0.8083 Sum Loss: 2.2128
Epoch 1/1... Discriminator Loss: 1.3923... Generator Loss: 0.9654 Sum Loss: 2.3577
Epoch 1/1... Discriminator Loss: 1.3808... Generator Loss: 0.9681 Sum Loss: 2.3489
Epoch 1/1... Discriminator Loss: 1.3142... Generator Loss: 0.8031 Sum Loss: 2.1173
Epoch 1/1... Discriminator Loss: 1.3414... Generator Loss: 0.7680 Sum Loss: 2.1095
Epoch 1/1... Discriminator Loss: 1.4128... Generator Loss: 0.9965 Sum Loss: 2.4092
Epoch 1/1... Discriminator Loss: 1.3322... Generator Loss: 0.8085 Sum Loss: 2.1407
Epoch 1/1... Discriminator Loss: 1.3755... Generator Loss: 0.7151 Sum Loss: 2.0906
Epoch 1/1... Discriminator Loss: 1.4207... Generator Loss: 0.7797 Sum Loss: 2.2004
Epoch 1/1... Discriminator Loss: 1.3869... Generator Loss: 0.8408 Sum Loss: 2.2277
Epoch 1/1... Discriminator Loss: 1.4288... Generator Loss: 1.0202 Sum Loss: 2.4490
Epoch 1/1... Discriminator Loss: 1.3266... Generator Loss: 0.8557 Sum Loss: 2.1823
Epoch 1/1... Discriminator Loss: 1.4690... Generator Loss: 0.9143 Sum Loss: 2.3833
Epoch 1/1... Discriminator Loss: 1.0955... Generator Loss: 1.3319 Sum Loss: 2.4274
Epoch 1/1... Discriminator Loss: 1.3906... Generator Loss: 0.7145 Sum Loss: 2.1051
Epoch 1/1... Discriminator Loss: 1.4275... Generator Loss: 0.7088 Sum Loss: 2.1363
Epoch 1/1... Discriminator Loss: 1.4623... Generator Loss: 0.7389 Sum Loss: 2.2013
Epoch 1/1... Discriminator Loss: 1.0800... Generator Loss: 1.8630 Sum Loss: 2.9430
Epoch 1/1... Discriminator Loss: 1.2762... Generator Loss: 0.9219 Sum Loss: 2.1981
Epoch 1/1... Discriminator Loss: 1.4753... Generator Loss: 0.9109 Sum Loss: 2.3862
Epoch 1/1... Discriminator Loss: 1.3793... Generator Loss: 0.8052 Sum Loss: 2.1845
Epoch 1/1... Discriminator Loss: 1.3558... Generator Loss: 0.7318 Sum Loss: 2.0876
Epoch 1/1... Discriminator Loss: 1.1679... Generator Loss: 1.1391 Sum Loss: 2.3070
Epoch 1/1... Discriminator Loss: 1.3613... Generator Loss: 0.8744 Sum Loss: 2.2357
Epoch 1/1... Discriminator Loss: 1.3601... Generator Loss: 0.7661 Sum Loss: 2.1262
Epoch 1/1... Discriminator Loss: 1.3485... Generator Loss: 0.7811 Sum Loss: 2.1296
Epoch 1/1... Discriminator Loss: 1.3639... Generator Loss: 0.7687 Sum Loss: 2.1326

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.